French - fluent manufacturing specialist challenging language models on industrial operations, quality, automation, and safety. Documenting model errors and improving prompt engineering and evaluation metrics.
About the role
- AI Native Engineer building AI-powered digital products at Valtech. Developing LLM, agentic and RAG solutions with modern software engineering practices.
Responsibilities
- Design, build and ship AI-native applications and experiences for real-world use cases
- Build applications using large language models, generative AI APIs and agentic architectures
- Develop high-quality software using Python, TypeScript/JavaScript or comparable modern languages
- Work with Azure OpenAI, OpenAI, Anthropic Claude, Google Gemini and open-source models
- Build RAG and knowledge-retrieval solutions using embeddings, semantic search, vector databases and enterprise data
- Experiment with AI agents, tool/function calling, structured outputs, orchestration and multi-step workflows
- Integrate AI solutions with APIs, databases, existing platforms and enterprise systems
- Rapidly prototype ideas, test assumptions and turn promising experiments into scalable solutions
- Build approaches for evaluating and testing AI systems for quality, accuracy, reliability, latency and cost
- Use AI-assisted engineering tools in software design, development, testing and maintenance
- Apply engineering practices around security, privacy, responsible AI, observability and human oversight
- Share learning and contribute to evolving AI engineering practices across Valtech
- Collaborate with multidisciplinary teams across engineering, data, experience and strategy
Requirements
- 2–4 years of professional software engineering or application development experience
- Strong programming experience with Python, TypeScript/JavaScript or another modern programming language
- Solid software engineering fundamentals, including application architecture, APIs, testing, debugging, version control and CI/CD
- Hands-on experience building with generative AI
- Practical experience with LLMs and model APIs, including prompting, context management, structured outputs and model selection
- Familiarity with RAG, embeddings, semantic search or vector databases
- Exposure to agents, function/tool calling or AI workflow orchestration
- Experience connecting applications to APIs, data sources, databases and cloud services
- Understanding of the difference between AI demos and dependable AI products
- Demonstrated habit of exploring emerging technology and turning curiosity into something tangible
Benefits
- Continuous learning opportunities
- Professional growth opportunities
- Workplace culture fostering creativity, diversity and autonomy
- Borderless, global framework enabling seamless collaboration
- Opportunities to apply emerging AI capabilities to meaningful client and business challenges
- Room to make, learn and grow
Job title
Job type
Experience level
Salary
Not specified
Degree requirement
No Education Requirement
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